I have built some neural networks (MLP (fully-connected), Elman (recurrent)) for different tasks, like playing Pong, classifying handwritten digits and stuff...
Additionally I tried to build some first convolutional neural networks, e.g. for classifying multi-digit handwritten notes, but I am completely new to analyze and cluster texts, e.g. in image recognition/clustering tasks one can rely on standardized input, like 25x25 sized images, RGB or greyscale and so on...there are plenty of pre-assumption features.
For text mining, for instance news articles, you have an ever changing size of input (different words, different sentences, different text length, ...).
How can one implement a modern text mining tool utilizing artificial intelligence, preferably neural networks / SOMs?
Unfortunately I were unable to find simple tutorials to start-off. Complex scientific papers are hard to read and not the best option for learning a topic (as to my opinion). I already read quite some papers about MLPs, dropout techniques, convolutional neural networks and so on, but I were unable to find a basic one about text mining - all I found was far too high level for my very limited text mining skills.